HPE_03.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HPE 03.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNpyvXfZXcC8fkulCVYz9sT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/a-forty-two/HPE-DecJanFeb2021/blob/main/HPE_03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ct-xO-KMoWeA",
        "outputId": "afbac288-b763-4efd-9774-8fbebb4a8761"
      },
      "source": [
        "# CLASSIFICATION\n",
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n",
            "1.15.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxkmrSbqprSX"
      },
      "source": [
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "fashion = keras.datasets.fashion_mnist"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vU0LOdoxqBz7"
      },
      "source": [
        "(trainimages,trainlabels), (testimages, testlabels) = fashion.load_data()\n",
        " "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbVHuityqnlZ",
        "outputId": "0772739b-04a9-4487-bbce-c7ae781cddfd"
      },
      "source": [
        "trainimages.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOmzFCFRqrcK",
        "outputId": "fea839f1-25f8-424b-d5fb-39c729f4b3a2"
      },
      "source": [
        "len(trainlabels)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLXZ6I8bqvPB",
        "outputId": "31af8fd7-5ea3-47e4-83f1-86e0265ecabe"
      },
      "source": [
        "len(testlabels)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "voGe7sGaq2Kv",
        "outputId": "b16fe101-9539-4bfb-970c-768824eb4056"
      },
      "source": [
        "classes = {0: 'T-shirt/top',\n",
        "1: 'Trouser',\n",
        "2: 'Pullover',\n",
        "3: 'Dress',\n",
        "4: 'Coat',\n",
        "5: 'Sandal',\n",
        "6: 'Shirt',\n",
        "7: 'Sneaker',\n",
        "8: 'Bag',\n",
        "9: 'Ankle boot'}\n",
        "\n",
        "x = 0\n",
        "plt.imshow(trainimages[x])\n",
        "print(classes[trainlabels[x]])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ankle boot\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNwyZ68MrUAz",
        "outputId": "29f2df13-d649-410a-d722-6fcd5cbb3463"
      },
      "source": [
        "for i in range(10):\n",
        "  print(classes[trainlabels[i]])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ankle boot\n",
            "T-shirt/top\n",
            "T-shirt/top\n",
            "Dress\n",
            "T-shirt/top\n",
            "Pullover\n",
            "Sneaker\n",
            "Pullover\n",
            "Sandal\n",
            "Sandal\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "IGPMvkGQtaTf",
        "outputId": "697ddbb8-520c-4235-a2dd-2d977a35d4be"
      },
      "source": [
        "\n",
        "x = 5\n",
        "plt.figure()\n",
        "plt.imshow(trainimages[x], cmap='gray')\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "print(classes[trainlabels[x]])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pullover\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "LQuB9gspt2Bc",
        "outputId": "2fccbd64-373b-4fd4-9868-6db72652bf85"
      },
      "source": [
        "# Smallest-> 0, Largest-> 255\n",
        "# Each pixel-> 1 byte-> 2^8-1\n",
        "# Entire dataset/ largest_num -> \n",
        "# smallest/largest-> value>0\n",
        "# largest/largest-> 1\n",
        "trainx = trainimages /255.0\n",
        "testx = testimages/255.0\n",
        "\n",
        "x = 5\n",
        "plt.figure()\n",
        "plt.imshow(trainx[x], cmap='gray')\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "print(classes[trainlabels[x]])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pullover\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ME3wIlRYu8cU"
      },
      "source": [
        "# y = 1000*w1 + 10*w2 + 0.0003 *w3 + 0.04\n",
        "# w -> 0..1 \n",
        "# VERY Large numbers, away from smaller numbers\n",
        "\n",
        "# 2 square = 4\n",
        "# 0.2 square = 0.04 \n",
        "# -0.2 square = 0.04 "
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnQOMi3sw9P1",
        "outputId": "c416d8e5-c714-4137-86e7-b70411d3144d"
      },
      "source": [
        "hp1_m1_hidden1 = 128\n",
        "l1_m1= keras.layers.Flatten(input_shape=(28,28))\n",
        "l2_m1= keras.layers.Dense(hp1_m1_hidden1, activation=tf.nn.relu)\n",
        "# y1=m1x+c1 ... y128 = m128x+c128\n",
        "l3_m1= keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "m1 = keras.Sequential([l1_m1,l2_m1,l3_m1])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FX0cjnTV0nad"
      },
      "source": [
        "hp1_m2_hidden1 = 64\n",
        "l1_m2= keras.layers.Flatten(input_shape=(28,28))\n",
        "l2_m2= keras.layers.Dense(hp1_m2_hidden1, activation=tf.nn.relu)\n",
        "# y1=m1x+c1 ... y128 = m128x+c128\n",
        "l3_m2= keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "m2 = keras.Sequential([l1_m2,l2_m2,l3_m2])"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVsnaLqV1GAc"
      },
      "source": [
        "hp1_m3_hidden1 = 64\n",
        "hp2_m3_hidden2 = 128\n",
        "l1_m3= keras.layers.Flatten(input_shape=(28,28))\n",
        "l2_m3= keras.layers.Dense(hp1_m3_hidden1, activation=tf.nn.relu)\n",
        "l3_m3= keras.layers.Dense(hp2_m3_hidden2, activation=tf.nn.relu)\n",
        "l4_m3= keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "m3 = keras.Sequential([l1_m3,l2_m3,l3_m3, l4_m3])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52eWmmO01qux"
      },
      "source": [
        "# x1*w1 + x2*w2 +b1 = y1 * w3 + y2 * w4 + y3 * w5+ b2\n",
        "# Neural Networks\n",
        "# 1) Look at the data-> try to build y=mx+c \n",
        "#. -> cost, time\n",
        "# 2) REPEATATION -> timelines -> EPOCH (Hyperparameter)\n",
        "# m1-> EPOCHS (100), m1-> EPOCHS (5)-> diff outputs!!!\n",
        "# 3) What happens in each EPOCH\n",
        "# -> Maximize my profits, Minimize my losses\n",
        "# -> Optimization Fn (correction to reduce the loss)\n",
        "#.        -> Adaptive Learning (adaptive learning rate)-> Adam\n",
        "#         -> prop, \n",
        "# -> Loss Fn (calculation of how much mistake was made) \n",
        "# -> metrics-> measure of how Loss is calculated [accuracy]\n",
        "\n",
        "\n",
        "m1.compile(optimizer='adam', \n",
        "           loss='sparse_categorical_crossentropy', \n",
        "           metrics=['accuracy'])\n",
        "m2.compile(optimizer='adam', \n",
        "           loss='sparse_categorical_crossentropy', \n",
        "           metrics=['accuracy'])\n",
        "m3.compile(optimizer='adam', \n",
        "           loss='sparse_categorical_crossentropy', \n",
        "           metrics=['accuracy'])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4JcpNvP5A5O",
        "outputId": "1cd13d7e-2797-4812-b30a-cfe2fedfbca7"
      },
      "source": [
        "m1.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6p8fWJK75N6q",
        "outputId": "72131e88-0471-4928-a5ec-30df455da637"
      },
      "source": [
        "m2.summary()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 50,890\n",
            "Trainable params: 50,890\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbOKvokB6Bu2",
        "outputId": "4f3d0f7b-6c8f-44f1-8282-5653e285f5a5"
      },
      "source": [
        "m3.summary()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_3 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 59,850\n",
            "Trainable params: 59,850\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3FpB5HM6GZd",
        "outputId": "7b35c026-e5a4-4fd7-fcd0-ea1ce55b456e"
      },
      "source": [
        "# Resuming at 7:45 \n",
        "HP_epochs = 5\n",
        "history1 = m1.fit(trainx, trainlabels, epochs=HP_epochs)\n",
        "history2 = m2.fit(trainx, trainlabels, epochs=HP_epochs)\n",
        "history3 = m3.fit(trainx, trainlabels, epochs=HP_epochs)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.5012 - acc: 0.8239\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 65us/sample - loss: 0.3778 - acc: 0.8632\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 65us/sample - loss: 0.3368 - acc: 0.8773\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.3131 - acc: 0.8848\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.2967 - acc: 0.8898\n",
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.5161 - acc: 0.8191\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.3924 - acc: 0.8593\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.3539 - acc: 0.8713\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.3299 - acc: 0.8802\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 3s 56us/sample - loss: 0.3130 - acc: 0.8848\n",
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 63us/sample - loss: 0.5055 - acc: 0.8184\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 63us/sample - loss: 0.3744 - acc: 0.8633\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 63us/sample - loss: 0.3380 - acc: 0.8759\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 63us/sample - loss: 0.3183 - acc: 0.8817\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 63us/sample - loss: 0.3003 - acc: 0.8878\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHAy48sV8kc_",
        "outputId": "a6c4bb0f-d9a6-4cf4-9adb-3319975895ea"
      },
      "source": [
        "history1.history"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'acc': [0.8239167, 0.8631667, 0.87733334, 0.88476664, 0.8898],\n",
              " 'loss': [0.5011600944201151,\n",
              "  0.3778417750736078,\n",
              "  0.33683658874034883,\n",
              "  0.3130897683123747,\n",
              "  0.29671518041491507]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5M9qk7IFAr5v",
        "outputId": "c5429c4f-c18e-4dcc-d1af-9b590585fac5"
      },
      "source": [
        "testloss1, testacc1 = m1.evaluate(testx, testlabels)\n",
        "testloss2, testacc2 = m2.evaluate(testx, testlabels)\n",
        "testloss3, testacc3 = m3.evaluate(testx, testlabels)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 39us/sample - loss: 0.3609 - acc: 0.8700\n",
            "10000/10000 [==============================] - 0s 36us/sample - loss: 0.3563 - acc: 0.8728\n",
            "10000/10000 [==============================] - 0s 36us/sample - loss: 0.3649 - acc: 0.8649\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebb4GNm1BcCs"
      },
      "source": [
        "predictions1 = m1.predict(testx)\n",
        "predictions2 = m2.predict(testx)\n",
        "predictions3 = m3.predict(testx)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KU-ERbRTBzDf",
        "outputId": "4be5881e-b77c-47e2-f4df-6e1b3fec04a8"
      },
      "source": [
        "predictions1[0]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.8866094e-07, 5.9875411e-08, 1.3793735e-07, 2.0879646e-07,\n",
              "       3.8256380e-07, 1.8256545e-02, 7.7140697e-07, 3.3833854e-02,\n",
              "       4.3838871e-05, 9.4786382e-01], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIhqg7LUBA5A",
        "outputId": "aa74971c-8608-4d76-d4cc-62d272f74761"
      },
      "source": [
        "for x in range(50):  \n",
        "  p1 =classes[np.argmax(predictions1[x])]\n",
        "  p2 =classes[np.argmax(predictions2[x])]\n",
        "  p3 =classes[np.argmax(predictions3[x])]\n",
        "  actual =classes[testlabels[x]]\n",
        "  didErrorHappen = False\n",
        "\n",
        "  if(p1 != actual):\n",
        "    print('Model 1 Says= ' + classes[np.argmax(predictions1[x])])\n",
        "    didErrorHappen = True\n",
        "  if(p2 != actual):\n",
        "    print('Model 2 Says= ' + classes[np.argmax(predictions2[x])])\n",
        "    didErrorHappen = True\n",
        "  if(p3 != actual):\n",
        "    print('Model 3 Says= ' + classes[np.argmax(predictions3[x])])\n",
        "    didErrorHappen = True\n",
        "  if(didErrorHappen):\n",
        "    print('Actual= ' + classes[testlabels[x]])\n",
        "    #plt.figure()\n",
        "    #plt.imshow(testx[x], cmap='gray')\n",
        "    #plt.colorbar()\n",
        "    #plt.grid(False)\n",
        "    "
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model 1 Says= Sandal\n",
            "Model 2 Says= Sandal\n",
            "Model 3 Says= Sandal\n",
            "Actual= Sneaker\n",
            "Model 1 Says= Pullover\n",
            "Model 2 Says= Pullover\n",
            "Actual= Coat\n",
            "Model 1 Says= Sandal\n",
            "Model 2 Says= Sandal\n",
            "Model 3 Says= Sandal\n",
            "Actual= Ankle boot\n",
            "Model 1 Says= Pullover\n",
            "Model 2 Says= Pullover\n",
            "Model 3 Says= Pullover\n",
            "Actual= Coat\n",
            "Model 1 Says= Dress\n",
            "Actual= T-shirt/top\n",
            "Model 1 Says= T-shirt/top\n",
            "Model 2 Says= T-shirt/top\n",
            "Actual= Shirt\n",
            "Model 2 Says= Shirt\n",
            "Model 3 Says= Shirt\n",
            "Actual= Dress\n",
            "Model 2 Says= Ankle boot\n",
            "Actual= Sneaker\n",
            "Model 3 Says= Shirt\n",
            "Actual= Pullover\n",
            "Model 1 Says= Shirt\n",
            "Model 2 Says= Shirt\n",
            "Model 3 Says= Shirt\n",
            "Actual= Pullover\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mc-42n-DBWfa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}